Conceptual clustering in information retrieval

Clustering is used in information retrieval systems to enhance the efficiency and effectiveness of the retrieval process. Clustering is achieved by partitioning the documents in a collection into classes such that documents that are associated with each other are assigned to the same cluster. This association is generally determined by examining the index term representation of documents or by capturing user feedback on queries on the system. In cluster-oriented systems, the retrieval process can be enhanced by employing characterization of clusters. In this paper, we present the techniques to develop clusters and cluster characterizations by employing user viewpoint. The user viewpoint is elicited through a structured interview based on a knowledge acquisition technique, namely personal construct theory. It is demonstrated that the application of personal construct theory results in a cluster representation that can be used during query as well as to assign new documents to the appropriate clusters.

[1]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[2]  John G. Gammack.  Different Techniques and Different Aspects on Declarative Knowledge , 1987 .

[3]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[4]  Shi Zhongzhui.  Knowledge-based decision support system , 1987 .

[5]  Kenneth M. Ford,et al.  Knowledge acquisition from repertory grids using a logic of confirmation , 1989, SGAR.

[6]  Vijay V. Raghavan,et al.  Assignment of term descriptors to clusters , 1990, Proceedings of the 1990 Symposium on Applied Computing.

[7]  John H. Boose,et al.  A Knowledge Acquisition Program for Expert Systems Based on Personal Construct Psychology , 1985, Int. J. Man Mach. Stud..

[8]  Bernhard Nordhausen,et al.  Conceptual Clustering Using Relational Information , 1986, AAAI.

[9]  Ellen M. Vdorhees,et al.  The cluster hypothesis revisited , 1985, SIGIR '85.

[10]  Vijay V. Raghavan,et al.  Description of the UNL/USL system used for MUC-3 , 1991, MUC.

[11]  B. John Oommen,et al.  Deterministic Learning Automata Solutions to the Equipartitioning Problem , 1988, IEEE Trans. Computers.

[12]  Wray L. Buntine,et al.  Induction of Horn Clauses: Methods and the Plausible Generalization Algorithm , 1987, Int. J. Man Mach. Stud..

[13]  John H. Boose,et al.  Expertise transfer for expert system design , 1986 .

[14]  John S. Edwards,et al.  Knowledge Acquisition for Expert Systems: A Practical Handbook , 1989 .

[15]  B. John Oommen,et al.  Fast object partitioning using Stochastic learning automata , 1987, SIGIR '87.

[16]  Vijay V. Raghavan,et al.  User-oriented document clustering: a framework for learning in information retrieval , 1986, SIGIR '86.

[17]  Ellen M. Vdorhees.  The cluster hypothesis revisited , 1985, SIGIR 1985.

[18]  Vijay V. Raghavan,et al.  Formation of Categories in Document Classification Systems , 1989, Great Lakes Computer Science Conference.

[19]  Bob J. Wielinga,et al.  Knowledge Acquisition for Expert Systems , 1987, Advanced Topics in Artificial Intelligence.

[20]  Clement T. Yu.  Adaptive document clustering , 1985, SIGIR '85.

[21]  Vijay V. Raghavan,et al.  Automatic cluster assignment for documents , 1990, [1991] Proceedings. The Seventh IEEE Conference on Artificial Intelligence Application.

[22]  G. Kelly.  The Psychology of Personal Constructs , 2020 .

[23]  Vijay V. Raghavan,et al.  Optimal determination of user-oriented clusters , 1987, SIGIR '87.

[24]  C. J. van Rijsbergen,et al.  The selection of good search terms , 1981, Inf. Process. Manag..

[25]  Brian R. Gaines,et al.  An Interactive Knowledge-Elicitation Technique Using Personal Construct Technology , 1987 .

[26]  Brian R. Gaines,et al.  Knowledge-support systems , 1990, Knowl. Based Syst..